Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)
Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature select...
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| Veröffentlicht in: | Contrast media and molecular imaging 2022, Vol.2022 (1), p.7693631-7693631 |
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| container_title | Contrast media and molecular imaging |
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| creator | Hu, Shuyi Lyu, Xiajie Li, Weifeng Cui, Xiaohan Liu, Qiaoyu Xu, Xiaoliang Wang, Jincheng Chen, Lin Zhang, Xudong Yin, Yin |
| description | Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P |
| doi_str_mv | 10.1155/2022/7693631 |
| format | Article |
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To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P<0.05. The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions. Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.</description><identifier>ISSN: 1555-4309</identifier><identifier>EISSN: 1555-4317</identifier><identifier>DOI: 10.1155/2022/7693631</identifier><identifier>PMID: 35833080</identifier><language>eng</language><publisher>Hindawi</publisher><ispartof>Contrast media and molecular imaging, 2022, Vol.2022 (1), p.7693631-7693631</ispartof><rights>Copyright © 2022 Shuyi Hu et al.</rights><rights>Copyright © 2022 Shuyi Hu et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c397t-83db738dd2a36b8df48f65e792cbf1950ddd51a462e9aedb55e7bf65ab8e5a443</citedby><cites>FETCH-LOGICAL-c397t-83db738dd2a36b8df48f65e792cbf1950ddd51a462e9aedb55e7bf65ab8e5a443</cites><orcidid>0000-0001-7918-9413 ; 0000-0001-8113-4278</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252683/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252683/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4024,27923,27924,27925,53791,53793</link.rule.ids></links><search><contributor>Teekaraman, Yuvaraja</contributor><creatorcontrib>Hu, Shuyi</creatorcontrib><creatorcontrib>Lyu, Xiajie</creatorcontrib><creatorcontrib>Li, Weifeng</creatorcontrib><creatorcontrib>Cui, Xiaohan</creatorcontrib><creatorcontrib>Liu, Qiaoyu</creatorcontrib><creatorcontrib>Xu, Xiaoliang</creatorcontrib><creatorcontrib>Wang, Jincheng</creatorcontrib><creatorcontrib>Chen, Lin</creatorcontrib><creatorcontrib>Zhang, Xudong</creatorcontrib><creatorcontrib>Yin, Yin</creatorcontrib><title>Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)</title><title>Contrast media and molecular imaging</title><description>Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P<0.05. The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions. Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.</description><issn>1555-4309</issn><issn>1555-4317</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><recordid>eNp9kV9L5DAUxcOysqPjvvkB8qiso0nTtM3LgtQ_I4iCzD6H2yQdI20ym7Qr8-3N0GFgX3w6l5wfJ9x7EDqj5IpSzq8zkmXXZSFYweg3dJye-CJntPx-mImYoZMY3wnJcybYDzRjvGKMVOQYhVfQ1vdWRXzjoNtGG7F3-Nk75d0QIA64XuHWB3xr42DderTxLQlemg0MViXtwa1TBODz5fICg9OT55XpurGDgGsIyrqJqOuLU3TUQhfNz73O0Z_7u1W9XDy9PDzWN08LxUQ5LCqmm5JVWmfAiqbSbV61BTelyFTTUsGJ1ppTyIvMCDC64clrEgFNZTikTefo95S7GZveaGV2-3RyE2wPYSs9WPm_4-ybXPt_UmQ8K9KB5uh8HxD839HEQfY27rYCZ_wYZYJEUYiSVgm9nFAVfIzBtIdvKJG7muSuJrmvKeG_JjydUsOH_Zr-BG1KkiM</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Hu, Shuyi</creator><creator>Lyu, Xiajie</creator><creator>Li, Weifeng</creator><creator>Cui, Xiaohan</creator><creator>Liu, Qiaoyu</creator><creator>Xu, Xiaoliang</creator><creator>Wang, Jincheng</creator><creator>Chen, Lin</creator><creator>Zhang, Xudong</creator><creator>Yin, Yin</creator><general>Hindawi</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-7918-9413</orcidid><orcidid>https://orcid.org/0000-0001-8113-4278</orcidid></search><sort><creationdate>2022</creationdate><title>Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)</title><author>Hu, Shuyi ; Lyu, Xiajie ; Li, Weifeng ; Cui, Xiaohan ; Liu, Qiaoyu ; Xu, Xiaoliang ; Wang, Jincheng ; Chen, Lin ; Zhang, Xudong ; Yin, Yin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c397t-83db738dd2a36b8df48f65e792cbf1950ddd51a462e9aedb55e7bf65ab8e5a443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Shuyi</creatorcontrib><creatorcontrib>Lyu, Xiajie</creatorcontrib><creatorcontrib>Li, Weifeng</creatorcontrib><creatorcontrib>Cui, Xiaohan</creatorcontrib><creatorcontrib>Liu, Qiaoyu</creatorcontrib><creatorcontrib>Xu, Xiaoliang</creatorcontrib><creatorcontrib>Wang, Jincheng</creatorcontrib><creatorcontrib>Chen, Lin</creatorcontrib><creatorcontrib>Zhang, Xudong</creatorcontrib><creatorcontrib>Yin, Yin</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Contrast media and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Shuyi</au><au>Lyu, Xiajie</au><au>Li, Weifeng</au><au>Cui, Xiaohan</au><au>Liu, Qiaoyu</au><au>Xu, Xiaoliang</au><au>Wang, Jincheng</au><au>Chen, Lin</au><au>Zhang, Xudong</au><au>Yin, Yin</au><au>Teekaraman, Yuvaraja</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)</atitle><jtitle>Contrast media and molecular imaging</jtitle><date>2022</date><risdate>2022</risdate><volume>2022</volume><issue>1</issue><spage>7693631</spage><epage>7693631</epage><pages>7693631-7693631</pages><issn>1555-4309</issn><eissn>1555-4317</eissn><abstract>Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P<0.05. The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions. Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.</abstract><pub>Hindawi</pub><pmid>35833080</pmid><doi>10.1155/2022/7693631</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-7918-9413</orcidid><orcidid>https://orcid.org/0000-0001-8113-4278</orcidid><oa>free_for_read</oa></addata></record> |
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| title | Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC) |
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